Robert B. Gramacy

Surrogates (eBook, ePUB)

Gaussian Process Modeling, Design, and Optimization for the Applied Sciences

• Format: ePub

Produktbeschreibung

Computer simulation experiments are essential to modern scientific discovery, whether that be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are meta-models of computer simulations, used to solve mathematical models that are too intricate to be worked by hand. Gaussian process (GP) regression is a supremely flexible tool for the analysis of computer simulation experiments. This book presents an applied introduction to GP regression for modelling and optimization of computer simulation experiments.

Features:
. Emphasis on methods, applications, and reproducibility.
. R code is integrated throughout for application of the methods.
. Includes more than 200 full colour figures.
. Includes many exercises to supplement understanding, with separate solutions available from the author.
. Supported by a website with full code available to reproduce all methods and examples.

The book is primarily designed as a textbook for postgraduate students studying GP regression from mathematics, statistics, computer science, and engineering. Given the breadth of examples, it could also be used by researchers from these fields, as well as from economics, life science, social science, etc.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 560
• Erscheinungstermin: 10. März 2020
• Englisch
• ISBN-13: 9781000766523
• Artikelnr.: 58786361

Autorenporträt

Robert Gramacy is a Professor of Statistics in the College of Science at Virginia Polytechnic and State University (Virginia Tech). Previously I was an Associate Professor of Econometrics and Statistics at the Booth School of Business, and a fellow of the Computation Institute at The University of Chicago. My research interests include Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, sequential design, and optimization under uncertainty.

Inhaltsangabe

1 Historical Perspective 2 Four Motivating Datasets 3 Steepest Ascent and
Ridge Analysis 4 Space-filling Design 5 Gaussian process regression 6
Model-Based Design for GPs 7 Optimization 8 Calibration and Sensitivity 9
GP Fidelity and Scale 10 Heteroskedasticity Appendix A Numerical Linear
Algebra for Fast GPs Appendix B An Experiment Game